Deep features based on a DCNN model for classifying imbalanced weld flaw types

Hou, Wenhui; Ye, Wei; Jin, Yi; Zhu, Changan

doi:10.1016/j.measurement.2018.09.011

Cited by 100 publications

(53 citation statements)

References 34 publications

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“…As a result, their model achieved an accuracy of 86.82%, much higher than classical models based on hand-crafted features. Similar work used data augmentation to diagnose furnace combustion states [140], weld flaw types [141] and balancing tail ropes' faults [142].…”

Section: ) Imagery Datamentioning

confidence: 99%

A Review on Deep Learning Applications in Prognostics and Health Management

et al. 2019

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Deep learning has attracted intense interest in Prognostics and Health Management (PHM), because of its enormous representing power, automated feature learning capability and best-in-class performance in solving complex problems. This paper surveys recent advancements in PHM methodologies using deep learning with the aim of identifying research gaps and suggesting further improvements. After a brief introduction to several deep learning models, we review and analyze applications of fault detection, diagnosis and prognosis using deep learning. The survey validates the universal applicability of deep learning to various types of input in PHM, including vibration, imagery, time-series and structured data. It also reveals that deep learning provides a one-fits-all framework for the primary PHM subfields: fault detection uses either reconstruction error or stacks a binary classifier on top of the network to detect anomalies; fault diagnosis typically adds a soft-max layer to perform multi-class classification; prognosis adds a continuous regression layer to predict remaining useful life. The general framework suggests the possibility of transfer learning across PHM applications. The survey reveals some common properties and identifies the research gaps in each PHM subfield. It concludes by summarizing some major challenges and potential opportunities in the domain.

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Section: ) Imagery Datamentioning

confidence: 99%

A Review on Deep Learning Applications in Prognostics and Health Management

et al. 2019

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“…The performance of deep feature was compared with those of traditional designed feature, such as texture features and histogram of oriented gradients features. The results showed that the separability of deep features is better [63]. Figure 4 shows the difference of traditional computer vision technology based on the above-mentioned technologies and the deep learning approach.…”

Section: New Methodsmentioning

confidence: 99%

Review on Computer Aided Weld Defect Detection from Radiography Images

Hou

Zhang

et al. 2020

Applied Sciences

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The weld defects inspection from radiography films is critical for assuring the serviceability and safety of weld joints. The various limitations of human interpretation made the development of innovative computer-aided techniques for automatic detection from radiography images an interest point of recent studies. The studies of automatic defect inspection are synthetically concluded from three aspects: pre-processing, defect segmentation and defect classification. The achievement and limitations of traditional defect classification method based on the feature extraction, selection and classifier are summarized. Then the applications of novel models based on learning(especially deep learning) were introduced. Finally, the achievement of automation methods were discussed and the challenges of current technology are presented for future research for both weld quality management and computer science researchers.

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“…Symmetry 2019, 11, 1440 3 of 14 In addition, the distribution of different types of printing defects might be unbalanced-that is, the sample number of the different types of printing defects is asymmetrical. However, the classifier is sensitive to an imbalance of samples, which will result in a high classification accuracy rate for the majority class but low accuracy for the minority class [13]. To this end, many researchers have addressed the classification problem with imbalanced samples.…”

Section: Overview Of the Proposed Methodsmentioning

confidence: 99%

A Deep Learning Based Printing Defect Classification Method with Imbalanced Samples

Zhang

et al. 2019

Symmetry

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Deep learning has been successfully applied to classification tasks in many fields due to its good performance in learning discriminative features. However, the application of deep learning to printing defect classification is very rare, and there is almost no research on the classification method for printing defects with imbalanced samples. In this paper, we present a deep convolutional neural network model to extract deep features directly from printed image defects. Furthermore, considering the asymmetry in the number of different types of defect samples-that is, the number of different kinds of defect samples is unbalanced-seven types of over-sampling methods were investigated to determine the best method. To verify the practical applications of the proposed deep model and the effectiveness of the extracted features, a large dataset of printing detect samples was built. All samples were collected from practical printing products in the factory. The dataset includes a coarse-grained dataset with four types of printing samples and a fine-grained dataset with eleven types of printing samples. The experimental results show that the proposed deep model achieves a 96.86% classification accuracy rate on the coarse-grained dataset without adopting over-sampling, which is the highest accuracy compared to the well-known deep models based on transfer learning. Moreover, by adopting the proposed deep model combined with the SVM-SMOTE over-sampling method, the accuracy rate is improved by more than 20% in the fine-grained dataset compared to the method without over-sampling.

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Deep features based on a DCNN model for classifying imbalanced weld flaw types

Cited by 100 publications

References 34 publications

A Review on Deep Learning Applications in Prognostics and Health Management

A Review on Deep Learning Applications in Prognostics and Health Management

Review on Computer Aided Weld Defect Detection from Radiography Images

A Deep Learning Based Printing Defect Classification Method with Imbalanced Samples

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